AI content quality: an editorial checklist before publishing

The trouble with most AI-assisted drafts is not that they read badly. They read fine. The sentences are grammatical, the structure is tidy and the tone is confident. That is exactly what makes them risky: a fluent draft can contain an invented statistic, an experience nobody had and an argument that says nothing new, and still pass a quick read.

So the question before publishing is not “does this sound human?” It is: is it true, is it useful, does it add something, and does it sound like us? Check factual claims and implied experience first, because an unsupported claim should stop publication on its own. Then check what the piece adds, then voice. Decide publish, revise or reject, with a named person accountable for the decision.

Start with what the draft must do for the reader

Before editing a sentence, write one line: after reading this, the reader should be able to ___. If you cannot finish that sentence, no amount of line editing will help, because the draft has no job. If you can, every section should serve that job, and anything that does not is a candidate for deletion.

This is also the fastest way to spot a common structural problem in AI drafts: a piece that covers a topic evenly instead of answering a question well. Covering is easy. Answering requires choosing what matters.

Audit claims, sources and implied experience

Go through the draft and mark every statement that could be true or false. For each one, ask where it comes from. In practice, these are the categories to check:

• Numbers and statistics. Every figure needs a source you have opened and read, not a search snippet and not the model’s memory.
• “Studies show” and “research suggests”. Which study? If the draft cannot name it, the sentence goes.
• Quotations. Check the exact wording against the original. A model can paraphrase a quotation and attribute it with complete confidence.
• Product and platform facts. Features, prices and policies change. Check the current official documentation.
• Implied first-hand experience. “In my experience”, “we’ve seen”, “our clients tell us”. A model cannot have had your experience. If the sentence is true, make it specific. If it is not, remove it.

My rule is simple: an unsupported material claim blocks publication. Not “revise if there is time”. It blocks. Everything else in this checklist is a matter of quality; this one is a matter of honesty, and once a reader catches one invented claim they stop trusting the rest.

Ask what the piece adds

A draft can be accurate and still not worth publishing. Ask: if a reader has already read the three best pages on this subject, what do they gain from ours? Good answers include:

• a worked example, with the method visible
• a clear judgement or recommendation, with its reasoning and limits
• evidence from your own work that you are allowed to share
• a tool the reader can use: a worksheet, a checklist, a decision table
• a better explanation of something others explain poorly

Google’s guidance on helpful, reliable, people-first content asks a similar set of questions, including whether content provides original information, research or analysis. It is a useful editorial lens whether or not search is the goal.

If the honest answer is “nothing”, the right decision may be not to publish, or to link to the better page instead.

Edit for voice, not synonyms

When a draft sounds generic, the tempting fix is to swap words: “utilise” for “use”, remove “delve”, vary the sentence openings. That changes the surface and leaves the problem. Generic writing is usually generic thinking: no specific example, no position taken, no trade-off admitted.

The edits that actually change the voice are:

• Replace abstractions with specifics. Not “improve efficiency” but “cut the Monday report from three hours to one”.
• Take a position. Say what you would do and why, and say when you would do something else.
• Admit the limit. Readers trust a writer who says what the method does not cover.
• Vary rhythm naturally. Some short sentences. Some longer ones that carry a chain of reasoning to its end.
• Cut the throat-clearing. The first paragraph of an AI draft can often be deleted without loss.

If your team has a written voice guide, check against it; if not, how to create a brand voice guide with AI explains how to build one that is usable.

An annotated edit

This is an editing exercise. The draft paragraph below was written deliberately for this article, with typical problems; it does not come from a real company or publication.

Draft:

In today’s fast-paced digital landscape, lead scoring has become a game-changer for B2B marketers. Studies show that companies using AI lead scoring see 50% more qualified leads. In my experience working with dozens of SaaS companies, the key is to leverage the right data. By implementing a robust scoring model, teams can unlock significant growth.

What the editor marks:

1. “In today’s fast-paced digital landscape”: says nothing. Delete.
2. “Studies show… 50% more qualified leads”: no study named, no definition of “qualified”, no comparison group. Blocks publication until sourced or removed.
3. “In my experience working with dozens of SaaS companies”: an experience claim the author has not verified. Blocks publication unless true, and then it needs a specific, permitted example.
4. “Leverage the right data”: which data? Vague.
5. “Robust scoring model… unlock significant growth”: a promise that cannot be tested.

Revision:

Lead scoring is only useful if it changes a decision your team actually makes, such as which enquiries sales reviews first. Before trusting a score, compare it with a simple rule on past enquiries where you already know the outcome. If the score does not rank the relevant enquiries higher than the rule does, it adds complexity, not value.

The revision is not better because the words are different. It is better because it makes a claim a reader can act on and check. The full method behind it is in how to evaluate AI lead scoring.

If you publish in two languages

A second language needs its own review, by someone who writes it well. Do not translate the English sentence by sentence: write the same argument with the same depth, in the rhythm of the other language. For Persian, the checks I would add are:

• Persian ی and ک, not the Arabic characters, and correct half-spaces (for example in «می‌شود»).
• Persian digits in prose and «» quotation marks.
• No calques of English idioms that read as translation, such as «کلید موفقیت» for “the key to success” everywhere.
• Watch the formal padding that AI output tends to produce in Persian: repeated «با بهره‌گیری از», «می‌باشد» and inflated openings like «در دنیای پرشتاب امروز».
• Technical terms introduced once with the English abbreviation, then used consistently.

Publish, revise or reject

Use this rubric for the final decision. Severity tells you what happens when a check fails: some failures block publication, some require revision, some are preferences the editor can accept.

Record the decision, the name of the person who made it and the main corrections. After a few pieces, the log will show recurring errors, such as an invented statistic turning up in every introduction, and you can fix them in the brief rather than in every edit.

A review prompt that asks for evidence

AI is useful as a second reader, if you ask it for specifics rather than a verdict. A prompt like “make this sound more human” produces a different set of generic sentences. A prompt that asks it to point at passages produces something you can check:

The model’s list is a starting point for the human review, not a replacement for it. It may miss claims, and it cannot check sources it has not seen.

Quality review takes time, and that time belongs in any honest assessment of what AI saves you: AI marketing ROI: measure value beyond time saved. For deciding which pieces deserve this effort in the first place, see B2B content strategy.